AI is already transforming how businesses operate, make decisions, and compete. But one question keeps coming up in our conversations with talent and tech leaders: 'When is the right time to make our first AI hire?'
We’re in the experimental phase of AI adoption. Many clients we're working with aren't hiring huge teams just yet, they're starting small, learning fast, and building confidence. There’s no one-size-fits-all approach, and we’re working with businesses taking different routes:
Rightmove partnered with Thoughtworks for a 12-month discovery phase before hiring a Head of AI & Data Science and building specialist teams.
Everway hired an EVP of AI to build and integrate GenAI capability into their core product offering.
Rothschilds brought in an AI Product Lead to drive internal adoption of a new AI product and influence stakeholders.
9fin chose to bring in specialist AI Engineers to complement the expertise they already had in-house. What they needed were dedicated ICs to focus and deliver.
B2B SaaS start-up Goodfit was required to submit an AI strategy as part of their Series A. Investors wanted a clear plan in place before any future funding would be considered.
The businesses gaining real value from AI aren’t just chasing a productivity boost, they’re using AI to uncover new revenue streams, spot threats early, and future-proof models. If AI is on your roadmap (or your investors are asking about it), now’s the time to act.
What should your 1st AI hire look like?
You don’t need a team to get started, just one person who's commercially-minded, curious, and able to connect AI with your business model. That could be:
🔧 AI Engineer – the most common starting point. A hands-on problem solver who can build MVPs, experiment, and test ideas with teams across the business.
🧠 AI Product Manager – increasingly in demand. Someone who understands your business deeply and can work cross-functionally to drive implementation.
📈 Strategist or Consultant – ideal if you're not sure where to begin. They can run a 3-month discovery phase and tell you what's possible.
⏳ Contractor vs Perm? – don’t be afraid to go contract for this first hire. If you're still figuring things out, a contractor can de-risk your first move whilst you learn.
👤 VP or Head of AI – if AI is core to your product or business strategy, bringing in leadership early gives you strategic oversight from day one and sets the foundations for future team growth.
This isn’t a 'code quietly in the corner' hire. They should be engaging across the business, understanding priorities, and translating them into real outcomes. Prioritise soft skills like communication, curiosity, and commercial awareness over ticking every technical box.
AI salaries: What to budget based on your goals
There’s no one-size-fits-all salary band for AI talent right now, it depends on what you’re looking for and how much value you place upon it.
For contractors:
£600–£700/day for a strong AI engineer exploring potential use cases.
£900–£1,000+/day for a senior consultant with a proven track record in discovery and implementation.
For permanent hires:
£110k–£130k for an engineer with solid experience and academic credibility.
£200k+ if you're hiring a senior leader or AI expert to lead strategy and transformation.
The key question to consider when deciding what you need is: 💭 Are you experimenting with AI, or rethinking your entire operating model?
Interviewing for impact: What to look for beyond technical skills
Technical assessments like Python or systems design tests are useful, but they're not the full story. We’re seeing a clear shift away from theoretical ML expertise towards applied GenAI with tangible business impact.
If someone has a PhD in Quantum Physics from Cambridge, their technical ability isn’t in question. What matters is how they think about the organisation. Can they spot opportunities and turn ideas into practical outcomes?
Some of the best approaches we’ve seen involve asking candidates to put together a short presentation on:
Where AI could add commercial value
How they'd explore and validate ideas
What data and context they’d need to succeed
🧑💻 Talent pool reality check:
Time-to-fill for AI Engineer roles is up ~40% vs. traditional engineering. Most candidates won’t have years of LLM production experience. We're seeing successful hires often come from adjacent backgrounds, like ML engineers with side projects, open-source contributions, or candidates from start-ups who've experimented heavily with R&D.
Job specs that are attracting great talent prioritise: ✔ Strong engineering foundations ✔ Curiosity and experimentation ✔ Self-driven learning over deep GenAI tenure
TL;DR: Getting your 1st AI hire right
💡 Your first AI hire will shape everything that follows. Prioritise commercial thinking over pure technical skill. The right individual can uncover opportunities, identify hidden risks, and define how AI scales across the business. Act early to stay ahead.
💡 Start small, but be strategic. You don’t need a full team on day one. Your hire could be an engineer, product lead, consultant, or VP - choose based on your goals and budget. Expect to pay anywhere from £110k–£200k+.
💡 Assess for impact, not perfection. Many great candidates come from non-traditional paths. Design your process to reveal curiosity, communication, and business acumen - the soft skills that will drive adoption and positive outcomes.
A closer look at the technical side of the evolving AI Engineer role: what they do day-to-day, why the role has emerged and how it differs from traditional ML positions.
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